Instructions to use wheattoast11/OmniCoder-9B-Zero-Phase2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use wheattoast11/OmniCoder-9B-Zero-Phase2 with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Tesslate/OmniCoder-9B")
model = PeftModel.from_pretrained(base_model, "wheattoast11/OmniCoder-9B-Zero-Phase2")

Transformers

How to use wheattoast11/OmniCoder-9B-Zero-Phase2 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="wheattoast11/OmniCoder-9B-Zero-Phase2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("wheattoast11/OmniCoder-9B-Zero-Phase2", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use wheattoast11/OmniCoder-9B-Zero-Phase2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "wheattoast11/OmniCoder-9B-Zero-Phase2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wheattoast11/OmniCoder-9B-Zero-Phase2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/wheattoast11/OmniCoder-9B-Zero-Phase2

SGLang

How to use wheattoast11/OmniCoder-9B-Zero-Phase2 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "wheattoast11/OmniCoder-9B-Zero-Phase2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wheattoast11/OmniCoder-9B-Zero-Phase2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "wheattoast11/OmniCoder-9B-Zero-Phase2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wheattoast11/OmniCoder-9B-Zero-Phase2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use wheattoast11/OmniCoder-9B-Zero-Phase2 with Docker Model Runner:
```
docker model run hf.co/wheattoast11/OmniCoder-9B-Zero-Phase2
```

OmniCoder-9B-Zero-Phase2

CARL Phase 1' — VLM grounding checkpoint. EVAL: PASS (94.6% click accuracy).

A LoRA adapter trained with vision GRPO for GUI grounding. The model understands screenshots and produces structured coordinate output for click targets.

Results

Metric	Value
Click accuracy	94.61%
Format compliance	100%
Eval samples	167 held-out
Status	PASS

Training

Method: Vision GRPO with CARL cascade rewards
Base model: Tesslate/OmniCoder-9B (Qwen3.5-9B VLM)
SFT substrate: wheattoast11/OmniCoder-9B-Zero-Phase2-Vision-SFT
Steps: 500 GRPO steps
Hardware: 1x L40S 48GB, bf16, LoRA r=64
Dataset: wheattoast11/grounding-with-images (20K samples)

Phase Transition Observed

During SFT, the model exhibited a first-order phase transition:

Steps 0-10: Baseline (3% accuracy, entropy 1.0)
Steps 10-20: Melting (entropy spikes to 9.3)
Steps 20-25: Transition (accuracy jumps 57 points in 5 steps)
Steps 25-35: Crystallization (99% accuracy, entropy 0.4)
Steps 35-46: Converged (99.3%, entropy 0.12)

Consistent with Kuramoto synchronization in coupled oscillator systems.

Theoretical Foundation

Bounded Informational Time Crystals — DOI: 10.5281/zenodo.18906944
Material Reality — DOI: 10.5281/zenodo.18992029
Semantic Realizability — DOI: 10.5281/zenodo.18992031

Usage

from transformers import AutoModelForImageTextToText, AutoProcessor
from peft import PeftModel

base = AutoModelForImageTextToText.from_pretrained(
    "Tesslate/OmniCoder-9B",
    torch_dtype="bfloat16",
    device_map="cuda:0",
)
model = PeftModel.from_pretrained(base, "wheattoast11/OmniCoder-9B-Zero-Phase2")
model = model.merge_and_unload()

processor = AutoProcessor.from_pretrained(
    "Tesslate/OmniCoder-9B",
    min_pixels=256*28*28,
    max_pixels=1280*28*28,
)

Citation

@article{desai2026carl,
  title   = {Coherence-Aware Reinforcement Learning},
  author  = {Desai, Tej},
  year    = {2026},
  url     = {https://github.com/wheattoast11/carl},
  note    = {Intuition Labs LLC}
}

License

Apache 2.0 — Intuition Labs LLC

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Model tree for wheattoast11/OmniCoder-9B-Zero-Phase2

Base model

Qwen/Qwen3.5-9B-Base

Finetuned

Qwen/Qwen3.5-9B

Finetuned

Tesslate/OmniCoder-9B

Adapter

(1)

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